Field of the Invention
The invention concerns techniques for evaluating image data from functional magnetic-resonance imaging, in particular techniques for differentiating correlation patterns of neurophysical events and false patterns within the framework of an independent component analysis.
Description of the Prior Art
Magnetic resonance imaging (MRI) can be used to analyze neurophysical events. MRI can be used to analyze functionally correlated regions of the brain (anatomical neural networks) with respect to neurophysical events. Anatomical neural networks can be visualized by identifying correlation patterns of neurophysical events. Herein, correlation patterns characterize a temporal and/or spatial correlation of neurophysical events.
One appropriate technique is functional MRI (fMRI). With fMRI, temporal changes to image contrast are depicted by suitable MRI measuring sequences. For example, it is possible observe a blood oxygenation level dependent contrast within the framework of fMRI, also known as BOLD contrast. This enables neurophysical events to be measured.
One special technique in fMRI is so-called resting-state fMRI (rsfMRI). With rsfMRI, account is taken of the time dependence between neurophysical events that are spaced apart in the spatial domain (functional connectivity), wherein the anatomical neural network is exposed to no external stimuli, or to no significant external stimuli. For example, with rsfMRI, the person under examination is not asked to perform any specific activities or think any specific thoughts (resting state).
With rsfMRI, extensive time series of three-dimensional image data are analyzed in order to investigate the mode of operation and correlations of cerebral activity in resting state. Accelerated imaging sequences enable a particularly high time resolution to be achieved. For example, a number of two-dimensional layers can be excited and read-out simultaneously in a region of interest of a person under examination. See, for example, Souza, S. P., et al. “SIMA: simultaneous multilayer acquisition of MR images by Hadamard-encoded excitation.” Journal of computer assisted tomography 12.6 (1988): 1026-1030; and Setsompop, Kawin, et al. “Blipped-controlled aliasing in parallel imaging for simultaneous multilayer echo planar imaging with reduced g-factor penalty.” Magnetic Resonance in Medicine 67.5 (2012): 1210-1224; and Breuer, Felix A., et al. “Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-layer imaging.” Magnetic resonance in medicine 53.3 (2005): 684-691.
The higher time resolution results in an increased amount of MR data. The data set to be analyzed is larger, as a result of which the computing-intensive preprocessing of the image data requires significantly more time. Typical time series take approximately 6-8 minutes and can depict, for example, 700-1000 volume regions with 70-80 slices.
With rsfMRI, different techniques are used to evaluate the time series of image data. An evaluation is based on an independent component analysis (ICA). The ICA is conventionally based on model assumptions in dependence on the current data record, such as seed points, which control the evaluation a-priori or descriptions of an external stimulus. This means that the correlation patterns can be identified without an a-priori restriction. With an ICA, correlation patterns, which can explain the intensity changes in the time series of image data as the consequence of neurofunctional events, are sought in the underlying image data. An ICA finds candidate correlation patterns that are independent of one another. These are also referred to as components of an ICA. These multiple candidate correlation patterns include correlation patterns of the number of neurophysical events and false patterns, which are obtained, for example, due to noise in the image data or are inevitable result of the ICA algorithm used. The false patterns are often of subordinate interest and should be rejected.
With conventional rsfMRI techniques, it can be complicated to differentiate between the correlation patterns of the neurophysical events and the false patterns. For example, it may be necessary to classify a large number of candidate correlation patterns, identified by the ICA manually, as relevant correlation patterns of the neurophysical events or as false patterns. This can be time-consuming and susceptible to error.
Therefore, there is a need for improved techniques for MRI with respect to evaluating neurophysical events in the brain of a person under examination. There is a need for rsfMRI techniques that rectify or alleviate at least some of the aforementioned drawbacks and restrictions.